Dynamic on-line optimization of production processes

ABSTRACT

A process is modeled by a dynamic model, handling time dependent relations between manipulated variables of different process sections ( 10 A-D) and measured process output variables. Suggested input trajectories for manipulated variables for a subsequent time period are obtained by optimizing an objective function over a prediction time period, under constraints imposed by the dynamic process model and/or preferably a production plan for the same period. The objective function comprises relations involving predictions of controlled process output variables as a function of time using the process model, based on the present measurements, preferably by a state estimation procedure. By the use of a prediction horizon, also planned future operational changes can be prepared for, reducing any induced fluctuations. In pulp and paper processes, process output variables associated with chemical additives can be used, adapting the optimization to handle chemical additives aspects.

TECHNICAL FIELD

[0001] The present invention relates generally to a method and a systemfor production processes and in particular to optimization of processoperation conditions. The present invention is particularly well suitedfor pulp and/or paper production processes.

BACKGROUND

[0002] In manufacturing of pulp and paper, the production is typicallydivided in a number of process sections connected to each other in amore or less complex pattern. Examples of process sections could bedigesters, washing arrangements, refiners, bleaching arrangements, etc.The part process of each process section is typically a complex process,where the output flow from the process section and its propertiesdepends on the present input flow (including its properties) ofmaterial, chemical additives, process operational conditions as well asof the previous history of operation of the process section. This meansthat the output is not only dependent on present conditions, but also onhow the process section was operated in an earlier stage, i.e. there isa dynamic relation between variables of different kinds.

[0003] In pulp and paper production, a lot of chemical substances areadded during the process. These process chemicals normally react andform other chemical substances when performing their intended actionupon the pulp and/or paper. However, some chemical substances are to alarge extent extracted from the process, to keep the concentrationswithin required limits. Since the chemical substances are expensive, asmuch as possible is collected and recovered. A pulp and paper productionline therefore typically comprises also process sections taking care ofextracted chemical substances. The flow and properties of such chemicalsubstances through the process are connected to operational conditionsin an even more complex manner. In particular, the dependence on processhistory is even more pronounced than for the pulp flow itself. In mostpulp and paper mills of today, control of the flow of chemical additivesis typically of a very simple type. If a shortage of chemicals appears,more chemicals are added, if an excess of chemicals appears, the excessis wasted.

[0004] In the European Patent Application EP 1 035 253, an on-lineoptimized pulp or paper production process is disclosed. In thisdisclosure, a number of inputs, such as raw material as well aschemicals, energy etc. are mentioned as important to optimize theprocess. The outputs, which are considered, are typically productionquantity, quality properties, and price as well as waste productquantities. However, the actual optimizing procedure is only describedin general terms as an automatically calibrating module. The method isprobably intended for off-line optimization of set-points in differenttypical steady-state situations. Difficulties arising from differencesin the previous history of operation are not addressed at all.Furthermore, the optimization basically concerns the process as oneentity, where only inputs and outputs of the entire process arediscussed, even if bottleneck problems are mentioned.

[0005] Problems arising from dependence of operation history becomesparticularly accentuated when the operation of the process is changed,e.g. if the production rate is changed. Also large variations in theproperties of the raw material, e.g. large kappa number changes or achange between hardwood and softwood, may cause large changes to theprocess. In such cases, large and slow fluctuations may be induced inthe process system. Some fluctuations may even have time constantsexceeding several hours. Models and optimization procedures, which arefocused in the present outcome of the process, may therefore introducecontrol measures, which much later may turn out to be unfavourable. Incases where the changes in process operation are large and/or abrupt, itmay not even be possible to maintain required quality, and operate themill close to the most profitable state. Fluctuations are generallyconnected with actions, which eventually end up with increased waste ofe.g. chemical additives, which in turn is connected to large costs.These fluctuations are difficult to handle in prior art control systems.

[0006] Control systems according to prior art are typically based on anassumption of a substantially failure-free operation. In case a failureoccurs and a process section temporarily has to be taken out ofoperation, there might not be enough buffers ensuring a continuousoperation for the rest of the process sections. Such discontinuities mayaffect both the quality and the quantity of the end product as well asother cost related properties. In particular in systems havingpronounced bottlenecks, problems with continuity may occur during minordisruptions.

SUMMARY

[0007] A problem with prior art control methods is that they typicallyare not suited for handling process operation variations havingrelatively large time constants. Another problem with prior art systemsis that there is a fairly large sensitivity even to minor failures ofprocess sections. Furthermore prior art systems typically have anunsatisfactory treatment of bottleneck processes.

[0008] An object of the present invention is thus to provide a systemand method, in particular for pulp and paper production, being capableof handling systems having dynamic processes with relatively large timeconstants. A further object of the present invention is to provide asystem and method, in particular for pulp and paper production that canoptimize the utilization of chemical additives in the process. Anotherobject is to provide a system and method offering a flexible possibilityto handle bottleneck problems.

[0009] The above objects are achieved by methods and systems accordingto the enclosed patent claims. In general words, a process is modeled bya dynamic model, handling time dependent relations between manipulatedvariables of different process sections and the process output variablesof respective process section. A number of state variables are measured,and previous measurements are preferably also available. Suggested inputtrajectories for manipulated variables for a subsequent time period areobtained by optimizing an objective function over a prediction timeperiod, under constraints imposed by the dynamic process model andpreferably a production plan for the same period. The objective functioncomprises relations involving predictions of controlled process outputvariables as a function of time for the prediction time period using theprocess model, based on the present and preferably also previousmeasurements of state variables. In such a manner, dynamic behaviors ofthe process are handled. By the use of a prediction horizon, alsoplanned future operational changes can be prepared for, reducing anyinduced fluctuations.

[0010] The dynamic model is preferably based on dynamic section models,modeling the actual process of the different process sections,interconnected by intermediate storage models, such as buffer models orbuffer tank models. Flows between the different sections are processoutput variables or manipulated variables. By using process outputvariables associated with chemical additives, and in particular theamount of chemicals, the distribution of chemicals over the system andthe consumption of chemicals, the optimisation can be adapted to handleopting aspects concerning chemical additives. Furthermore, otheroptimization aspects such as handling of bottleneck processes and minorfailures in exposed process sections are also treated. State estimationtechniques are preferably used for pre-treatment of the actuallymeasured variables to be able to generate an initial state for thefuture optimization.

[0011] The method and the system according to the invention may be usedto carry out on a production process, preferably a pulp and/or paperproduction process, any of the operations of: monitoring, controlling,regulating, simulating, optimising, providing support for decisions,advising.

BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The invention, together with further objects and advantagesthereof, may best be understood by making reference to the followingdescription taken together with the accompanying drawings, in which:

[0013]FIG. 1 is a block diagram illustrating a model of a part of aprocess system;

[0014]FIG. 2 is a diagram illustrating the effect of a temporarilyshut-down of a process section on buffer levels;

[0015]FIG. 3 is a diagram illustrating production plans, state variabletrajectories and input trajectories created by a method according to thepresent invention;

[0016]FIG. 4 is a diagram illustrating a principle of receding horizonusable with the present invention;

[0017]FIG. 5 is a flow diagram of an embodiment of a method according tothe present invention;

[0018]FIG. 6 is a diagram illustrating a state estimation process;

[0019]FIG. 7 is a block diagram illustrating a pulp and paper productionsystem according to an embodiment of the present invention; and

[0020]FIG. 8 is block diagram of a computer system useful in a systemaccording to the present invention.

DETAILED DESCRIPTION

[0021] In the present invention disclosure, a number of variablesconnected to the process will be discussed. These variables havesomewhat differing character, and to avoid any misinterpretation of theterminology, definitions of the terms used throughout the presentdisclosure is first presented.

[0022] A process is at every time characterized by a number of statevariables. Such state variables can in a general view be of almost anykind and is here used as a common term for all possible variablesconcerning the state of a process.

[0023] A process or a section thereof is typically controlled by settinga number of manipulated variables. Such manipulated variables are statevariables that are easy to influence and that have a significantrelevance for the operation of the process. Such manipulated variablescan e.g. be the input flow of material or additives into a process,easily regulated by controlling e.g. a valve.

[0024] The result of a control action is represented by a set of statevariables, in the present disclosure called process output variables. Asubset of these process output variables is suitable for monitoring thecontrol action. Such variables are here called controlled process outputvariables. The purpose of the control action is to bring the controlledprocess output variables close to pre-determined set-points or set-pointfunctions. State variables characterizing the result of the processcontrol, but not directly used for controlling purposes, are in thepresent disclosure called associated process output variables.

[0025] Some state variables are easy to measure more or less directly bydifferent sensors. Such variables are called measurable variables. Otherstate variables may be difficult or even impossible to measure in adirect manner. Instead, another related quantity or quantities is(are)measured, and the actual state variable is estimated through more orless complex models or relations. Such variables are called derivablevariables.

[0026] Many production processes of today are quite complex arrangementsof different linked process sections. Cost and environmentalconsiderations call for recycling of waste products, which makes theallover process system even more complex. There is normally not only onestraight line of process sections, but the sections are typicallyconnected in loops and by a network of connections.

[0027] A typical way to handle flow variations in the interface betweendifferent process sections is to introduce buffer tanks. The absoluteoutput flow from a preceding process section does not limit theoperation of a subsequent process section, if only the average outputflow is sufficient. The exact level of the buffer tank is of minorimportance in each moment.

[0028] General production processes involve today quite complexoperations. The manipulated variables used for performing the actualcontrol of the process section are generally not directly connected tothe process output variables, such as e.g. flow and outputcharacteristics by any simple relations. Non-linear relations as well astime dependencies are typically present. This means that a certainchange in a manipulated variable performed at different occasions mayhave different effect on the process output variables. Such non-linearand/or dynamic processes are e.g. present in most pulp and paperproduction mills of today. Since many of the processes basically arerelated to non-trivial chemical reactions, different chemicalequilibrium relations are for instance involved. Differentconcentrations of chemical species may drive the process according todifferent chemical reactions. There might also be a large inertia effectin such processes. An excess of a certain chemical species during oneprocess period may influence the process operation at a later occasion.

[0029] From the above discussion, it is obvious that a general pulp andpaper production process of today comprises material flows and processeshaving large time constants for variations. These dynamic propertiesemanate from the process dynamics itself, but also from the use ofbuffer storage tanks between process sections. Efforts for supportingsuch dynamics have to be employed when modeling such systems.

[0030] According to a preferred embodiment of the present invention, amodel of a complex production process is based on the division into anumber of standard model objects. The total model thereby comprises anumber of section models, corresponding to the different processsections, a group of process sections or a part of one process section.The section models are connected to each other via model objects,basically representing intermediate storages, here in the form of socalled buffer models. FIG. 1 illustrates an example of a model 1 of aprocess system. This process system portion has four process sections,represented by section models 10A-D. The section models are connected bya number of connections having different flows 30A-M. Each section model10A-D has one or more section input flow 30A, D, E, I, K, L of rawmaterial, part products, chemical additives, solvents etc. Each sectionmodel 10A-D has also one or more section output flow 30B, C, F, G, H, J,M of section end product, by-products, waste products etc. The sectionoutput flows 30B, C, F, G. H, J, M are in turn connected to constituteinput flows of buffer models 20A-H. Similarly, the section input flows30A, D, E, I, K, L starts as output flows from the buffer models 20A-H.In such a way, each section model 10A-D is typically connected to onlybuffer models 20A-H and buffer models 20A-H are only connected tosection models 10A-D. Additionally, there are input flows 32A-B to theentire process and output flows 34A-B from the entire process, whichflows are directed to or starting at buffer models 20A-H.

[0031] The principle of alternating sections and buffers is a simple andpreferred manner to design the total model by a limited number ofstandard model objects. However, anyone skilled in the art understandsthat other model designs may be used as well. For instance, the actionsof the buffers may be incorporated as a part of a neighboring sectionmodel, whereby only interconnected section models are present. The modelmay also be a mix there between and may include also other types ofmodel objects, having other characteristics.

[0032] In the model of FIG. 1, the section models 10A-D and the buffermodels 20A-H present different characteristics. A section model 10A-Drepresents a part process, and includes a more or less complex dynamicrelation between the input flows and the output flows, including theproperties of the flow substances. This relation has typically a numberof manipulated variables 40A-D, by which the actual control of theprocess is exerted. Such manipulated variables may e.g. control theinput flow rates to the process section, or operational conditions suchas temperature, pressure, energy supply etc. to the process section. Asdescribed above, the relation between input and output flows, and theproperties of the substances thereof, is generally a dynamic relation,involving time dependent terms. This means that the process outputvariables characterizing the operation of the process section generallydepends not only on the present settings of the manipulated variables,but also on previous settings of manipulated variables as well as on theactual previous process operation.

[0033] The intermediate storage model may be generalized to a few objecttypes. There is an input flow, basically determined by the output flowof one or several section models, or by external factors whenconsidering the total inputs 32A-B. There is an output flow, determinedby the input flow of one or several section models, or by externalfactors when considering the total (outputs 34A-B. A buffer level 22A-Hor a concentration is thus determined by the time-integrated differencesbetween input and output flow. Basically, the buffers are not possibleto control explicitly. In a first simple stage of modeling, the materialcontent in a buffer is considered to be homogeneous, i.e. an immediateand complete mixing is assumed. However, if large variations insubstance properties are expected, or if a mixing is believed to bedifficult, a more sophisticated model can be created, where timedependent relations between substance properties of input and outputflows are employed.

[0034] In a process system according to the model illustrated in FIG. 1,there are many process output variables that are of interest formonitoring the operation of the process. A number of different sensors51, 52, 53 are provided to measure important process output variables.In FIG. 1, only three sensors are illustrated, in order to simplify thedrawing. However, sensors are typically present allover the entiresystem. The sensor 51 may be representative of measurements of statevariables e.g. process conditions in the actual process sections. Thesensor 52 may be representative of measurements of flow rates andcomposition and/or concentration of substances in the flow. The sensor53 may be representative of a buffer level sensor of a buffer tank. Moreexamples of useful process output variables and their utilization arediscussed further below.

[0035] When creating a model of a complex process, not only the actualoperation of the process has to be considered, but also aspectsconcerning the combined operation of process sections and buffers. Inmost process systems, there are one or several bottlenecks. Suchbottlenecks typically represent process sections with the smallestoperational margin as compared with the intended maximum production ofthe total plant. In FIG. 1, it is assumed that process section 10Crepresents such a bottleneck. If said section 10C is not operated at orclose to its maximum capacity, there is a risk that the material storedin buffer 20F eventually will run out. This in turn means that processsection 10A has to be operated at a lower rate, which probably willreduce the overall production capacity. Thus, an efficient operation ofthe total process should include measures to ensure that the processsection 10C is operated at or close to its maximum capacity duringsubstantially all the time. One measure in this direction is to ensurethat there is always input material available in buffer 20E. The bufferlevel 22E of buffer 20E should therefore in a control procedure not beallowed to drop too far under normal operation conditions. Similarly,buffers 20F and 20G, in particular 20G that is dependent only on theprocess section 10C, have to have available buffer space for storing theoutput flow from the process section 10C. This means that e.g. thebuffer level of buffer 20G preferably should be kept comparably low, ifpossible.

[0036] In many process systems, certain process sections are normallymore exposed to operation failures than others. Examples of temporaryoperation stops or failures could be e.g. need for service of equipment,such as cleaning, replacing consumable parts or minor repairs. Byidentifying such “weak” process sections, the total productionefficiency may be ensured by introducing additional constraints orcontrol goals into the controlling procedure. If in FIG. 1, processsection 10B is subjected to intermittent operation failures, e.g. someconsumable parts have to be replaced at irregular and unpredictableoccasions, the following interesting considerations can for instance bemade. Assume that the average out-of-operation time is comparably largeand that there is knowledge of the statistical distribution of time torepair and time between failure. If the replacement is performed ratherquickly, the operation of the other process sections does not have to beaffected, if there is enough material buffered. If the process section110B should be allowed to have shorter inactivity periods, the bufferlevels of buffer 20B and 20C have to be kept generally low to allow fora collection of substances during the inactivity period. Similarly, thebuffer level in buffer 20F has to be kept generally high, so that aninterruption of the process section 10B does not affect process section10A. How buffer 20D is affected depends on the further connection of theoutput flow 34A.

[0037]FIG. 2 is a diagram illustrating what may happen at aninterruption of the process section 10B. It is assumed that the initialoperation is in steady state, i.e. such that the buffer levels are keptconstant in both buffer 20B and 20D, and that the output flow from thebuffer 20D is constant and the input flow to the buffer 20B also isconstant. The corresponding buffer levels are illustrated by the curves102 and 101, respectively. At a time t₁, process section 10B is takenout of operation and is put back in operation again at time t₂. In themeantime, the input flow to buffer 20B increases the buffer levellinearly and the output flow from buffer 20D decreases linearly. If thetime interval t₂-t₁ is short enough, the buffers 20B and 20D will notreach any critical limits. When the process section 10B comes intooperation again, the total process is controlled in such a way that thebuffer levels of buffer 20B and 20D slowly returns to the requestedideal values. The preferred buffer levels as well as the total buffervolume can thus be optimized by knowledge of the statisticaldistribution of stop-times of process section 10B.

[0038] If both the above scenarios are present at the same time, onerealizes that there are demands put on the buffer level in buffer 20F,which go in opposite directions. This calls for a very careful controlof the buffer level and/or a large maximum buffer volume.

[0039] In chemical pulp and paper processes of today, a number ofchemical additives are typically used. The most frequently appearingsubstances are sodium and sulfur in form of different chemicalcompounds. Sodium and sulfur thus exist in different appearances duringthe process, e.g. as sodium sulfide, sodium hydroxide, sodium sulfate,sodium tiosulfate, sodium sulfite, sodium carbonate, sodium oxalate,sulfuric acid, sulfur dioxide, elementary sulfur and organic sulfurcompounds. Most of the chemicals in the process are recovered andrecirculated into the process. In the different process sections of theproduction, sodium and sulfur are distributed in these differentcompounds in different concentrations. In some process sections thereare substantial losses of chemicals out from the process. Chemicals canalso be purged from the process in a controlled manner. Depending onwhere in the process these losses are present, the relation between theamounts of sulfur and sodium that are lost will correspond to therelative amounts and chemical state in the process section underconsideration. Such losses of chemical species involving e.g. sulfur andsodium have to be compensated by adding new chemical additives into theprocess. Also the balance between sodium and sulfur has to bemaintained.

[0040] Even if the cost for chemical additives is considerable, theadded amounts of chemical additives are generally very small comparedwith the total flow of chemicals used in the process. The recirculationprocess is therefore of crucial importance for the entire production.The process of recovering and recirculating e.g. sodium and sulfurcontaining chemical species is generally a complex and time-consumingprocess. It may e.g. comprise a number of chemical reaction steps,combustion as well as refinery and distillation processes. This meansthat there are relatively large amounts of sulfur and sodium present insuch parts of the total process that is not immediately involved in thepulp and paper processing. However, since these chemicals are going tobe recirculated, the operation of the entire process is anyway stronglyinfluenced by the recirculation process steps. Furthermore, therecirculation of chemicals induces very large time constants forconcentration variations of chemicals throughout the entire process.

[0041] The large time constants for chemical additives in the processand dead-times introduced by large volumes in the process result in thata change in chemical concentration at one place in the process will showup in other places in the process even several hours later. Most priorart systems apply control mechanisms that are based on presentoperational state for controlling e.g. provision of chemical additives.This may, however, instead induce substantial additional variations inchemical concentrations in the pulping process. For instance, thecomposition of the white liquor that is the active digester liquor, e.g.the sulfidity, can vary substantially and may furthermore be difficultto keep constant and to control to a requested value by prior-artmethods. A large variation in e.g. sulfidity is typically connected tounnecessarily high consumption of chemicals and increases thereby thecosts for chemical additives. This may also give rise to otherdisturbances eventually affecting the production quantity and/orquality.

[0042] The present invention provides an eminent possibility to operateor control the balance of chemicals throughout the entire system in adynamic manner. Composition variations are thereby reduced, which showsmainly as a reduced cost for chemical additives. Also, a more evenproduct quality is likely to achieve. It may therefore be important tocontrol the process on the total balance of chemical additives presentin the process, the distribution of different chemicals between and/orconcentrations of different chemicals within the different processsections, as well as on the total amount of chemicals consumed by theprocess. The above presented dynamic model allows for a dynamicdescription of each individual process section as well as for a dynamicdescription of the distribution of e.g. sodium and sulfur through e.g.the buffer tanks. Such a model can therefore be used also to predictfuture sodium and sulfur distributions. The present invention isespecially well-suited for controlling a pulp and paper productionprocess and in particular the chemical balance within such a process.The present invention is also especially well-suited to be used forproviding an operator of a pulp and/or paper production process adviceand/or decision support. The method and the system according to theinvention may however be used to carry out any of the operations of:monitoring, controlling, regulating, simulating, optimising, providingsupport for decisions, advising.

[0043] A production process is as mentioned further above characterizedby a multitude of state variables or in particular process outputvariables. In e.g. a pulp and paper production process, there are a lotof quantities to measure. First of all, flows of material betweendifferent process sections and buffers are of particular interest, sincethey normally constitute either manipulated variables or process outputvariables of the process sections. The flow rate is therefore ofimportance, but also the composition, in particular concentrations ofdifferent chemical species, of the material flow. In a pulp and paperprocess, typical flow-related state variables are total volume (or flowrate), ratio of suspended solids, ratio of dissolved solids, sodiumconcentration, hydroxide concentration, hydrosulfide concentration,sulfate concentration, carbonate concentration and total amount reducedsulfur. Most of these variables are measurable variables that can bemeasured on-line, or alternatively, samples can be extracted andanalyzed off-line.

[0044] In the intermediate storage, the level can be monitored and ifrequested also the composition of the material within the tank, and thenin particular concentrations of different chemical species.

[0045] Since the processes in the process sections normally aredependent also on a number of external operational factors, such astemperature, pressure, supplied power, etc. such state variables mayalso be useful to measure.

[0046] Some of the measured state variables are also of interest forcontrol purposes, i.e. they constitute controlled process outputvariables. As mentioned above, general process consideration such asbottlenecks or failure risk may put some restrictions on some of theprocess output variables. Quality consideration may call for a morecareful control of other process output variables, such as white liquorconcentration, discussed above. Controlled process output variables,such as extracted quantities of derivatives of chemical additives, mayalso be useful for the control procedure in order to be able to monitorthe flow of chemicals and try to reduce the consumption thereof. In eachprocess system, a number of controlled process output variables that areuseful for monitoring important aspects of the process are thus selectedand goal functions are formulated. Associated process output variablesmay be of great importance as input parameters into the dynamic processmodel, but are of less importance for the direct control action.

[0047] Manipulated variables of the different process sections are usedfor controlling the entire process. These manipulated variables do nothave to be directly connected to the controlled process outputvariables, but in a general case there are more or less complexrelations between the manipulated variable values and the effectsthereof on the controlled process output variables. Typical manipulatedvariables of the processes are input flow rates, temperature, pressure,supplied power, duration of different process steps etc.

[0048] One important aspect of controlling a process by use of thepresent invention is connected with prediction of future processoperation. Many production lines today are designed to be able toproduce different amounts, types or qualities of products. There is ageneral trend to increase the abilities to modify the productionaccording to the present status of e.g. sales. If a certain quality ofpulp or paper is particularly requested during a certain period, theproduction could be turned over to produce more of that pulp or paperquality. In case the demands for paper are reduced, the production couldbe turned to only pulp production, which may be easier to sell or tostore for future use. Production changes are common today and the trendis that the duration of each production mode is reduced more and more,and also that changes of production modes have become even more flexibleaccording to production on customer demand.

[0049] Other changes of the production mode can be caused e.g. byplanned changes in e.g. raw material properties. In a pulp and paperproduction system, a change between hardwood and softwood may call fordifferent operational modes.

[0050] The same type of control situation may arise at plannedmaintenance work of the plant. If certain parts need maintenance, theproduction may be modified in such a way that the maintenance can beperformed without shutting down the entire mill. This may be performedby either changing the production as a whole or by preparing the systemin advance by building up sufficient buffers.

[0051] If a sudden change of operational mode is carried out and thecontrol of the process is based substantially only on the present state,this will typically induce fluctuations in process output variables. Inorder to bring controlled process output variables closer to requestedgoal values, changes in other process output variables will appear atother places in the system, at later occasions. This behavior isgenerally unwanted, since it typically results in e.g. higherconsumption of chemical additives. Another approach could be togradually change operational mode, but this will in a typical case givean end product that also gradually changes its properties between theinitial and final operational modes. Also, unintentional transientdisturbances such as pump failure etc. may occur quite frequently in aproduction process system.

[0052] By using methods and systems according to the present invention,the control of a process is not only dependent on present values ofstate variables, but also on previous values of state variables as wellas predicted future values of state variables. Future known events canthen be prepared for and operation optimization can be performed alsotaking such changes into account. By letting some state variables onpurpose drift away from their ideal values, in order to prepare for asmooth transformation into the new operational mode, an overallproduction advantage can be achieved. Some illustrative examples areshown further below.

[0053] The dynamic process model can in a mathematical sense beexpressed as a differential algebraic equation system:

F[x(t),{dot over (x)}(t),u(t),t]=0

[0054] where x denotes state variables and u manipulated variables.Measurements or estimations of measurable and derivable process outputvariables can be expressed as:

y(t)=g(x(t),t)

[0055] The model is also associated with model constraints, e.g. limitsfor different manipulated variables and/or process output variables:

a≦u_(k)≦b

d≦x_(k)≦e

[0056] There might also be different more or less complex inequalityconstraints:

c ^(k)(x _(k) ,u _(k))≦0.

[0057] By using the dynamic model with measured present and preferablyalso previous process output variables as parameters, a present or“initial” state of the process can be estimated. Starting from e.g. theproduction plan and taking a number of additional constraints andobjectives into account, target trajectories for the selected controlledprocess output variables can be formulated. Such optimizing aspectscould concern e.g. bottlenecks, frequent failing objects and otherdirectly production related aspects. In a pulp and paper plant,optimizing aspects connected to chemical additives are particularlyimportant. Target trajectories for certain concentrations or flows ofchemical additives are then of interest. The target trajectories are ina general case time dependent, i.e. functions of time. The targettrajectories take constraints imposed by optimizing aspects intoaccount, e.g. the allowed range for different process output variablescould be restricted.

[0058] The control method according to the present invention performs anoptimization procedure, in which optimum input trajectories of themanipulated variables are created. The optimization is performedminimizing an objective function. The objective function is formulatedin accordance with the optimizing aspects and is preferably based on acomparison between the target trajectories of the controlled processoutput variables and controlled process output variables as predicted bythe dynamic process model. The computation is based on present values ofstate variables. The objective function is minimized by varying theinput trajectories for the manipulated variables. The input trajectoriesgiving the minimum of the objective function is thereby stated to be theoptimum input trajectories.

[0059] The goal for the entire process is then to operate the process insuch a way that the controlled process output variables are kept asclose as possible to respective target trajectory, with acceptableefforts and with pre-determined weights for competing targets. Theproduction plan is typically included as one of the targets.

[0060] A process system, such as a pulp and paper plant is usually sucha complex system that an optimization performed directly against idealtarget trajectories for the controlled process output variables oftenbecomes relatively unstable. The model details and the computationalefforts needed for a stable optimization are often not realistic inpractice. This becomes more critical upon rapid changes in theoperational mode of the system, both planned and unplanned changes. Auseful approach to overcome these problems is to use referencetrajectories as target trajectories in the optimization process. Thereference trajectories are modified, typically smoothened, idealset-point trajectories, taking the initial state and future plannedoperational mode changes into account. An illustrative example will begiven here below.

[0061] In FIG. 3, a production plan 110, 11 and its influence on theprocess operation are illustrated. At the bottom, the production plan isillustrated. Until a time t₃, paper of a certain paper quality Q1 shouldbe produced at a pre-determined rate R1. After time t₃, the productionis planned to change to a paper quality Q2 with a production rate of R2.In the middle part of FIG. 3, an ideal set-point trajectory s(t) for acontrolled process output variable is illustrated. The controlledprocess output variable is measured, either directly or indirectly.Preferably, as discussed more in detail further below, the measuredcontrolled process output variable is treated in a state estimationprocess. The estimated present value of the process output variable isillustrated as a cross 115, in general somewhat offset from the idealset-point trajectory. In a control process allowing all parameters tovary in any combination it would be natural to request that the actualcontrolled process output variable should reach the ideal set-pointtrajectory as fast as possible, i.e. e.g. according to the dotted curve114. However, fast changes in control variables induce fluctuations inthe process conditions, and the actual processes may put physicallimitations on the rapidness of response to control actions.Furthermore, competing optimization considerations may also deterioratethe stability of the control process.

[0062] A better way in a typical practical case would be to control thecontrolled process output variable in a smoother manner. A referencetrajectory r(t) is instead created, being a modification of the idealset-point trajectory with a smoothened behavior. In the first part, e.g.an exponentially decreasing difference between the ideal set-pointtrajectory s(t) and the reference trajectory r(t) is assigned. Closer tot₃, where the ideal set-point trajectory s(t) exhibits a step, thereference trajectory r(t) will deviate exponentially to reach the meanvalue at the step time and then again smoothly approaching the idealset-point trajectory s(t). In this example, an exponential form wasselected, but anyone skilled in the art realizes that any suitablesmooth curve shape can be used for such purposes. The referencetrajectory r(t) will then be used as the target trajectory for theoptimization process. The actual trajectory may look like curve 116.

[0063] When performing the optimization, the reference trajectories ofseveral controlled process output variables are used and a weightingbetween different optimizing aspects is performed. One optimized inputtrajectory for manipulated variable relatively closely related to theprocess output variable of FIG. 3, is illustrated in the top part ofFIG. 3. It is here seen that the control action caused by the changingprocess mode at t3 starts in advance and forms a smooth translation.

[0064] The target trajectories of different controlled process outputvariables are in a general case not entirely compatible with each other.In a typical case, there has to be some trade-off between the differentcontrolled process output variables. This is made by formulating theobjective function in a proper manner. A difference between the actualvalue of a controlled process output variable and the correspondingtarget trajectory is often used and can be weighted, as a compromisebetween different competing targets.

[0065] A general optimization problem can be expressed in a discreteformulation with a sampling time of ΔT, as:$\min\limits_{u^{k},{k = \hat{k}},\ldots \quad,{\hat{k} + K - 1}}J$

[0066] where J is the objective function, considering constraintsimposed by the model and/or production plan, t=kΔT, and K is the numberof samples in prediction horizon, {circumflex over (k)} is the latestsample point and u^(k) is a manipulated variable. In a typical pulp andpaper case the objective function could be composed by several competingaspects:

J=J _(fiber need) +J _(conc) +J _(chem) +J _(prod loss).

[0067] An example of one of the terms could e.g. be:${J_{conc} = {\sum\limits_{k = \hat{k}}^{\hat{k} + K}{q_{conc}\left( {x_{k} - x_{{ref}\quad k}} \right)}^{2}}},$

[0068] where q is a weight factor for different chemical species. Aconcentration term as the one above thus favors a stable concentration.The objective function involves a kind of time integration, consideringthe J values at all sampling events k. A time factor may also beimplemented in the objective function, e.g. to weight the near futuredifferently from the far future:$J_{conc} = {\sum\limits_{k = \hat{k}}^{\hat{k} + K}{{w\left( {k - \hat{k}} \right)}\quad {{q_{conc}\left( {x_{k} - x_{{ref}\quad k}} \right)}^{2}.}}}$

[0069] Terms of near future can then be assigned a larger weight thanmore time distant terms.

[0070] Terms involving a total amount of a chemical species in one orseveral process sections favors a reduction of this species in theseprocess secions. When aiming for reducing a total amount for the entireprocess, at least two process sections are preferably involved.

[0071] For e.g. bottleneck problems, terms involving buffer capacityand/or buffer levels before and after the bottleneck process sectionsare used. One useful term is dependent on remaining buffer capacity of abuffer preceding a bottleneck process section. Another useful term isdependent on the actual buffer level of a buffer following a bottleneckprocess section.

[0072] For process section having a relative high probability offailure, the terms are preferably defined in the opposite manner. Oneuseful term is dependent on remaining buffer capacity of a bufferfollowing the process section in question, while another useful term isdependent on the actual buffer level of a buffer preceding the processsection.

[0073] For solving the optimization problem for a complex process, somekind of solver has to be used. The more complex the system is, the morecomputational capacity is needed. There are several solvers ofoptimization problems available today. The proper choice has to bedetermined in relation to the actual system that is going to becontrolled. However, the actual procedure of performing the computationof the optimization takes place according to known principles and is notparticularly important for the basic invention idea, and the details aretherefore left out from this description.

[0074] Thus, by letting the objective function depend on not only thepresent situation, but also on predicted future situations, the overalloperation can be improved. This is particularly important for processfeatures having fluctuations with large time constants. The best exampleof such process features known to us is the total amount of and thedistribution of chemical additives through a pulp and paper productionprocess.

[0075] A complex process, such as pulp and paper production, gives riseto a quite complicated optimization problem. A large number of variablesare to be optimized according to relations and constraints, and thechoice of solver should be performed considering also this. Although theprocessor capacities of today are quite impressive, the time required toperform the actual calculations may take several minutes. However, sincethe typical time constants for changes within pulp and paper systems arelong, a total time for measuring, state estimation and optimization ofseveral minutes may still be short enough to perform an actual on-linecontrol of the process. The sampling time ΔT, mentioned above, may in atypical case be e.g. less than 15 minutes.

[0076] Even if the present invention today is believed to be applicablemore or less only to slowly reacting processes, increased computationalspeed may change these preferred areas of applications. Fasteroptimizations opens up the use of the method also for faster respondingsystems.

[0077] The output of the optimizing procedure is as described above aset of input trajectories for the manipulated variables of the process,i.e. recommended settings to an operator as a function of time. Theprocess can thereby be operated or controlled manually, by setting themanipulated variables of the process manually according to the suggestedinput trajectories. An operator may then include his experience to avoidoperational mistakes. True on-line operation or control can also beaccomplished, where the control variables automatically are set to theinput trajectory values.

[0078] In FIG. 3, the input trajectory is a piecewise constant functionof time, i.e. the manipulated variables are adjusted intermittently.Such an extra constraint on the manipulated variables is in agreementwith the discretized formulation of the optimization and can be put inorder to facilitate e.g. manual control, since it is difficult for anoperator to continuously change a larger number of manipulated variablesettings. However, if an automatic control is applied, the manipulatedvariable settings and hence the input trajectories may be continuouslychanging.

[0079] The production plan and state variable trajectories arecalculated for a certain time interval, in the present disclosure calledprediction time interval Δt₁. The prediction time interval shouldpreferably be selected to be longer than the time constants of anymonitoring state variable variation. In a typical pulp and/or paperprocess, the prediction time interval can with advantage be 12 hours oreven longer. Any variations in process output variables that are inprogress will then be considered in the calculation processes. However,the accuracy of the prediction will decrease with increased predictiontime interval length. The end of the trajectories will therefore not beas accurate as the first parts thereof.

[0080] Moreover, the input trajectories suggested for control purposessuffer the same accuracy declination as the predictions from the model.This means that the first part of the input trajectories is ratheraccurate, while the end part of the trajectories are impaired with largeuncertainties. It is therefore not suitable to use the entire inputtrajectory at once. A practical solution on this problem is to use a“receding horizon” approach. The principles are illustrated in FIG. 4.With this approach, the entire optimization is performed over theprediction time interval Δt₁, represented by a controlled process outputvariable reference trajectory 120. However, only a first small portionof the input trajectories 121 (of which one is illustrated in thefigure) is used for control purposes. This time portion is in thepresent disclosure called the control time interval Δt₂. The controltime interval is preferably substantially shorter than the predictiontime interval and may e.g. be equal to the sampling time mentionedearlier. Typically, the control time interval is more than ten timesshorter than the prediction time interval. When the end of the controltime interval Δt₂ approaches, new measurements, new predictions and anew optimization is performed with the new prediction time interval Δt₁*moved forward to begin substantially at the end of the previous controltime interval. A new optimisation is performed, resulting in new statevariable trajectories 122 and new input trajectories 123. The controloperation can hence be performed during the new control time intervalΔt₂*. The procedure can continue by successively move the predictiontime interval and the control time interval forward. In this way, theaccuracy is guaranteed at the same time as an optimum time dependence ofthe optimization horizon is ensured. Furthermore, a previous inputtrajectory is believed to form a very advantageous start approximationfor the following optimization.

[0081] Due to the finite computational speed, there is typically acertain time difference between the actual measuring time and the startof the corresponding control time interval. However, such timedifferences are compensated for in the model and optimization routines.

[0082] In the optimization procedure, the start point or the initialstate of the process is used. This initial state is estimated by usingthe model and present and preferably also previous measurements ofdifferent quantities associated with process output variables. Sinceboth the model and the measurements suffer from noise and uncertainties,a preferred embodiment of the present invention employs some kind ofstate estimation procedure. When taking noise into account, the dynamicprocess model may be represented as:

x _(k+1) =f(x _(k) ,u _(k))+w _(k),

[0083] where w_(k) represents model noise. Similarly, observations maybe expressed as:

y _(k) =g(x _(k))+v _(k),

[0084] where v_(k) represents measurement noise. A state estimationprocess may then be expressed as:${{\min\limits_{x_{0},{k = 0},\ldots \quad,{\hat{k} - 1}}{\sum\limits_{k = 0}^{T - 1}{{v_{k}}^{2}R^{- 1}}}} + {{w_{k}}^{2}Q^{- 1}}},$

[0085] where R and Q are the covariance matrix of measurement deviationuncertainty and uncertainty of the model, respectively, giving aweighting between the uncertainty of measurements and model,respectively. In this manner an estimated initial state is obtained,which ideally depends on the present measurements as well as on previousmeasurements.

[0086] An approach to implement state estimation is to use a movinghorizon estimation. This is schematically illustrated in FIG. 6. Adiagram illustrates a series of measurements 130 of a process outputvariable at different sampling times. A frame 131 defines a certainnumber of previous measurements, which are going to be used in the stateestimation process. The previous measurements are put into the dynamicprocess model and minimized regarding the measurement noise and themodel uncertainty. From the optimization using measurements from theentire frame 131, an estimated value 132 based on the present measuredvalue is provided, which is believed to resemble the actual value 133 ina better manner. The estimation is thus performed using the dynamicprocess model in a historical perspective. The following optimization ofthe control procedure uses the same fundamental dynamic process model ina future perspective instead.

[0087] The actual state estimation can also be performed using othertechniques, e.g. Kalman filter techniques. Different state estimationsmay be optimal for different process systems, depending on the actualdesign of the process model.

[0088] As a summary, an embodiment of a general method according to thepresent invention is illustrated in a flow diagram in FIG. 5. Theprocess starts in step 200. In step 202, process output variables areobtained. In step 204, this data is validated and in step 206 a stateestimation is performed in order to determine an initial state.Constraints for a future prediction horizon Δt₁ are specified in step208. Preferably a production plan for the prediction horizon interval isprovided. In step 210, objective function parameters are specified forthe future prediction horizon. This objective function depends onpredicted controlled process output variables. Preferably, the method isapplied on pulp and paper production, where at least one controlledprocess output variable is associated with the amount, distributionand/or consumption of chemical additives. In step 212, inputtrajectories for manipulated variables for the prediction time intervalare created by an optimization process. The input trajectoriescorrespond to those manipulated variables that optimize the objectivefunction, under the constraints. Finally, the process is controlled instep 214 by setting the manipulated variables according to the inputtrajectories during a control time interval Δt₂. The process ends instep 216. Preferably, the steps of the present flow diagram arerepeated. The state estimation step 206, and the optimizationcomputation 212 are performed using a dynamic process model. The dynamicprocess model has time dependent relations between manipulated variablesof process sections of the process and process output variables of thesame process section.

[0089] The methods according to the present invention may be implementedas software, hardware, or a combination thereof. A computer programproduct implementing the method or a part thereof comprises software ora computer program run on a general purpose or specially adaptedcomputer, processor or microprocessor. The software includes computerprogram code elements or software code portions that make the computerperform the method using (at least one of the steps previously describedin FIG. 5. The program may be stored in whole or part, on, or in, one ormore suitable computer readable media or data storage means such as amagnetic disk, CD-ROM or DVD disk, hard disk, magneto-optical memorystorage means, in RAM or volatile memory, in ROM or flash memory, asfirmware, or on a data server. Such a computer program product can alsobe supplied via a network, such as Internet.

[0090] In FIG. 7, an embodiment of a pulp and paper process system 2according to the present invention is illustrated schematically. Theillustrated process system 2 is based on the sulfate process principle.In the sulfate process, an initial input flow consists of wood chips82A. A flow of wood chips 80A enters a digester 60A, where the chips arecooked at approximately 170° C. with a liquor, called white liquor 80P,containing OH— and HS— ions as active species. Lignin is then separatedfrom the carbohydrates, cellulose and hemicellulose in the fibers. Thecooking can be done in either a continuous or a batch process. After thecooking, the pulp is washed (not shown in the figure) and provided as aflow 80B. Pulp 80C is further delignified in an oxygen delignificationstage 60B. After oxygen delignification, pulp 80D, 80E is transported toa bleach plant 60C. Bleached pulp 80F, 80G is supplied to a papermachine 60D for the actual papermaking process step. Paper 84A leavesthe plant as the output product.

[0091] Liquor spent from the cooking 60A containing the coolingchemicals and the dissolved organic substance is called weak blackliquor 80H. The black liquor 80I is brought to and evaporated in theevaporation plant 60E from a dry solid content from below 20% to 65-80%dry solid content. The evaporated black liquor is provided to a recoveryboiler 60F, where it is combusted. In the combustion, the energy of theorganic substance is recovered in the form of high-pressure steam. Theinorganic substance is separated as a smelt at the bottom of the boiler.The sulfur compounds are here reduced to sulfide (S²⁻). The smeltconsists mainly of sodium carbonate and sodium sulfide. The smelt isdissolved in water and the resulting liquor 80L is called green liquor.The sulfide is hydrolyzed to OH⁻ and HS⁻ when it is brought into contactwith the water. The green liquor 80M is provided to a recausticizingdepartment 60H. Here, the carbonate is transformed to OH⁻— ions by thereaction with Ca(OH)₂. The solid calcium carbonate that is used isburned to CaO in a lime kiln 60I and thereafter slaked to give Ca(OH)₂represented by 80S, which is entered via 80T into the recausticizingdepartment 60H. The slurry 80N is filtrated in filter 60G to give whiteliquor 800, the rest 80Q is returned as an input 80R to the lime kiln601. Finally, the white liquor 80P is returned to the digester 60A.Make-up chemicals are added, such as NaOH in 82B and Na₂SO₄ in 82C toreplace losses and to adjust sodium and sulfur imbalances.

[0092] The sodium and sulfur balance is adjusted by purging ESP-dust 84B(Electro-Static Static Precipitator) from the recovery boiler 60F.ESP-dust 84B consists mainly of Na₂SO₄ and some Na₂CO₃. The balancebetween sodium and sulfur is defined by the sulfidity of the whiteliquor. If the sulfidity is above the set point, sulfur has to be purgedby ESP-dust. Since the ESP-dust also contains sodium, sodium will belost. This loss has to be covered with NaOH make-up. If the sulfidity isbelow the target, sulfur has to be added to the process (for example asNa₂SO₄ in 82C).

[0093] Several buffer tanks are needed in the process. Wood chips arestored in 70A.

[0094] Pulp of different quality is stored in 70B-D. Weak black liquoris stored in 70E, prior to evaporation. Strong black liquor 80J with ahigh dry-solid C content is stored in 70F. Tank 70G contains greenliquor and 70H contains white liquor. In tank 70I, lime mud (mainlyCaCO₃) is stored prior to reburning and the burned lime (mainly CaO) isstored in 70J before slaking/recausticizing.

[0095] Several process output variables are measured e.g. effectivealkali (EA), degree of caustization and sulfidity represented by 56.This can be done either with on-line sensors or at the processlaboratory. Flow rates, represented by 57, consistencies represented by58 and buffer tank levels represented by 59 are also measured at severalpositions.

[0096] In a preferred control method for the system illustrated in FIG.7, a number of optimizing aspects are used. One goal for the opt izationaccording to the present invention is to minimize the discrepancy ofcertain concentrations of chemical species with regard to recommendedset-point values. In particular the sulfidity of the white liquor 80Pprovided to the digester 60A is important to control to keep thesulfidity of the digester 60A as constant as possible close to a desiredlevel. It is thus preferred to select the sulfidity in the digester 60Aas one of the controlled process output variables. It is also desirableto keep the total cost for chemicals down, and therefore the additions82B and 82C should be kept as low as possible, as well as the outletESP-dust flow 84B. This means that the flows 82B, 82C and 84B alsoshould be selected as controlled process output variables. In apreferred operational mode, there are certain relations between amountand concentration of certain chemical species in different parts of theprocess. Since the concentration of sulfur in form of different chemicalcompounds in the different liquors is of importance, concentrations ofdifferent chemical species in the flows 80H-T are interesting to be usedas controlled process output variables as well.

[0097] Furthermore, the optimization to minimize production losses atminor production disturbances and to minimize the effects of bottleneckprocesses are typically based on controlled process output variables inthe form of buffer levels of buffer tanks before and after identifiedproblem sections.

[0098] Optimizing criteria, such as keeping the production rateaccording to the production plan, to minimize the variations in fiberneed at the paper machine 60D etc. are also included as terms in theobjective function.

[0099] The described method is preferably implemented, as illustrated inFIG. 8 by a suitable network topology, in a separate server connected toan internal mill computer network 13. The network will typically beconnected to a domain server 12. A server 14 where the method isimplemented will typically also include functionality to exchange datawith the process control system and other servers 15 containing relevantdata as e.g. process history data storage systems, laboratory datastorage systems 3 etc. A preferable way to exchange data is via OPC(Object Linking and Embedding for Process Control) 4. OPC is aspecification requiring all data sources to show the same type ofinterface. OPC is a well-known specification published by the OPCfoundation. It is based on Microsoft's Component Object Model (COM).

[0100] The server 14 could e.g. use the Aspect Integration Platform(AIP-server) as the communication layer between the described method andthe mill information. This approach makes it possible to access theserver 14 via separate workplaces 5, which in case of the AspectIntegration Platform is used could be clients including Operate IT. Theinternal mill computer network is often connected to an intranet 6 via arouter 7. This makes it possible to access the described method viaworkplaces with thin clients 8, using Internet Explorer. Via a firewall9, the described method could also be accessed via computers connectedto the Internet 11. A system with Internet connection does as analternative also allow for the method to be implemented in a server at aremote location. The service provider at this remote location will thenhave a similar network topology as the mill network, as indicated inFIG. 8 (13 b, 14 b, 6 b, 7 b and 9 b). The server 14 at the mill network13 is in this case not needed. The described method will in this case beinstalled in an equivalent server (14 b) at the remote network. Thisremote location may be at a service provider's office. From this remotelocation several client mills may simultaneously be optimised andcontrolled in real time by using the described method.

[0101] The present invention is described by examples from pulp andpaper industry, and in particular connected to optimization of the useof chemical additives. These examples are especially well suited forapplying the present invention. However, also other optimizationconsiderations may be used with the present invention. The invention canalso be applied to other process production systems with processsections and/or intermediate storages in, (for example the chemicalindustry, the petrochemical industry (refineries etc.), pharmaceuticaland food industry, consumer industry and metal and minerals industry.

[0102] It will be understood by those skilled in the art that variousmodifications and changes may be made to the present invention withoutdeparture from the scope thereof, which is defined by the appendedclaims.

REFERENCES

[0103] European Patent Application EP 1 035 253

1. A method for a production process having a number of process sectionsthe method comprising: obtaining a dynamic process model having timedependent relations between manipulated variables for said processsections and process output variables from respective process section;providing external constraints for said production process for aprediction time interval; measuring a set of process output variables ofsaid production process; estimating an initial state by using saidmeasured process output variables; defining an objective functioninvolving predicted controlled process output variables for saidprediction time interval and said external constraints; said predictedcontrolled process output variables being defined by said dynamicprocess model based on said initial state; optimizing said objectivefunction under constraints imposed by said dynamic process model and/orsaid external constraints, by adapting said manipulated variables,giving input trajectories for said manipulated variables for saidprediction time interval; and operating said production process bysetting said manipulated variables according to said input trajectoriesduring a control time interval.
 2. The method according to claim 1,wherein said method is a pulp and/or paper production method.
 3. Themethod according to claim 2, wherein said controlled process outputvariables comprise at least one variable associated with chemicaladditives used for pulp and/or paper production.
 4. The method accordingto claim 3, wherein said defining step in turn comprises the step ofincluding an objective function term being dependent on a total amountof chemical additives in at least two of said process sections.
 5. Themethod according to claim 3, wherein said defining step in turncomprises the step of including an objective function term beingdependent on the relative distribution of different chemical forms ofchemical additives between different process sections throughout theproduction process.
 6. The method according to claim 3, wherein saiddefining step in turn comprises the step of including an objectivefunction term being dependent on a difference between a concentration ofat least one chemical additive in at least one of said process sectionsand a pre-determined set value.
 7. The method according to claim 6,wherein said concentration is related to sulfidity.
 8. The methodaccording to claim 1, wherein said external constraints comprises aproduction plan.
 9. The method according to claim 1, wherein saidestimation of said initial state is also obtained by previously measuredprocess output variables.
 10. The method according to claim 9, whereinsaid estimation of said initial state comprises a state estimationprocedure.
 11. The method according to claim 10, wherein said stateestimation procedure is a moving horizon state estimation.
 12. Themethod according to claim 1, wherein said control time interval is apart of said prediction time interval.
 13. The method according to claim12, wherein said control time interval is substantially shorter thansaid prediction time interval.
 14. The method according to claim 13,wherein said prediction time interval is more than 10 times longer thansaid control time interval.
 15. The method according to claim 1 whereinat least a part of said measuring of process output variables isperformed on-line.
 16. The method according to claim 1 wherein at leastone of said process output variables is selected from the list of: flowrate; flow concentration; buffer level; buffer concentration; andinternal process section variable.
 17. The method according to claim 1wherein said defining step in turn comprises the step of: derivingtarget trajectories for said controlled process output variables undersaid external constraints starting from said initial state; whereby saidobjective function comprises deviations between said target trajectoriesand predicted process output variables integrated over said predictiontime interval.
 18. The method according to claim 17, wherein saidderiving step in turn comprises the steps of: calculating idealset-point trajectories for said controlled process output variablesunder constraints imposed by said production plan; and modifying saidideal set-point trajectories into said target trajectories byintroducing said initial state and a smoothing with time of structuresof said ideal set-point.
 19. The method according to claim 17, whereinsaid integration over said prediction time interval further comprises atime dependent weight function.
 20. The method according to claim 1wherein said dynamic process model in turn comprises a number of sectionmodels, representing operation of a process section, connected to anumber of intermediate storage models by model flows; said sectionmodels comprising said time dependent relations; and said intermediatestorage models being characterized by a buffer level.
 21. The methodaccording to claim 20, wherein defining step in turn comprises the stepof including an objective function term being dependent on remainingbuffer capacity of a buffer preceding a bottleneck process section andon said buffer level of a buffer following a bottleneck process section.22. The method according to claim 20, wherein defining step in turncomprises the step of including an objective function term beingdependent on said buffer level of a buffer preceding a process sectionhaving a relative high probability of failure and on remaining buffercapacity of a buffer following a process section having a relative highprobability of failure.
 23. The method according to claim 1, wherein oneor more steps of said method is performed at a location remote from saidproduction process.
 24. A production process system, comprising: anumber of process sections, controllable by manipulated variables;sensors measuring a set of process output variables of said productionprocess; processor means, connected to said sensors; process sectioncontrol means, setting said manipulated variables, said process sectioncontrol means being connected to said processor means; said processormeans in turn comprising: means for obtaining a dynamic process modelhaving time dependent relations between manipulated variables for saidprocess sections and process output variables from respective processsection; means for providing external constraints for a prediction timeinterval; means for defining an objective function involving predictedcontrolled process output variables for said prediction time intervaland said external constraints; said predicted controlled process outputvariables being defined by said dynamic process model based on anestimation of an initial state obtained by said measured set of processoutput variables; means for optimizing said objective function underconstraints imposed by said dynamic process model and/or said externalconstraints by adapting said manipulated variables, giving inputtrajectories for said manipulated variables for said prediction timeinterval; whereby said process section control means is arranged forsetting said manipulated variables according to said input trajectoriesduring a control time interval.
 25. A system for a production processhaving a number of process sections, controllable by manipulatedvariables, comprising: processor means; and process section controlmeans, setting said manipulated variables, said process section controlmeans being connected to said processor means; said processor means inturn comprising: sensor input means for receiving a set of processoutput variables of said production process; means for obtaining adynamic process model having time dependent relations betweenmanipulated variables for said process sections and process outputvariables from respective process section; means for providing externalconstraints for a prediction time interval; means for defining anobjective function involving predicted controlled process outputvariables for said prediction time interval and said externalconstraints; said predicted controlled process output variables beingdefined by said dynamic process model based on an estimation of aninitial state obtained by said measured set of process output variables;means for optimizing said objective function under constraints imposedby said dynamic process model and/or said external constraints byadapting said manipulated variables, giving input trajectories for saidmanipulated variables for said prediction time interval; whereby saidprocess section control means is arranged for setting said manipulatedvariables according to said input trajectories during a control timeinterval.
 26. The system according to claim 25, wherein said productionprocess system is a pulp and/or paper production process system.
 27. Thesystem according to claim 26, wherein said controlled process outputvariables comprise at least one variable associated with chemicaladditives of said production process.
 28. The system according to claim27, wherein said objective function comprises a term dependent on atotal amount of chemical additives in at least two of said processsections of said production process.
 29. The system according to claim27 wherein said objective function comprises relations based on therelative distribution of different chemical species of chemicaladditives between different process sections throughout the productionprocess.
 30. The system according to claim 27 wherein said objectivefunction comprises a term dependent on a difference of a concentrationof at least one chemical additive in at least one of said processsections and a pre-determined set-value.
 31. The system according toclaim 30, wherein said concentration is related to sulfidity.
 32. Thesystem according to claim 25 wherein said external constraints comprisea production plan.
 33. The system according to claim 25 wherein saidestimation of said initial state is obtained also by previously measuredprocess output variables.
 34. The system according to claim 33, whereinsaid processor further comprises state estimation means for performingsaid estimation of said initial state.
 35. The system according to claim25 at least one of said process output variables of said productionprocess system has a time constant exceeding 12 hours.
 36. The systemaccording to claim 25 said prediction time interval exceeds 12 hours.37. The system according to claim 25 said control time interval is lessthan 15 minutes.
 38. The system according to claim 25 whereincommunication links between said processor and said production process,for allowing a remote control of said production process.
 39. The systemaccording to claim 38, wherein said communication links comprises a datacommunication network.
 40. A computer program product comprisingcomputer code means and/or software code portions for making a processorperform the steps of claim
 1. 41. The computer program product accordingto claim 40 supplied via a network, such as Internet.
 42. A computerreadable medium containing a computer program product according to claim40.
 43. A computer program comprising computer code means and/orsoftware code portions for making a processor perform the steps ofclaim
 1. 44. The computer program according to claim 43 supplied via anetwork, such as Internet.
 45. Use of a method according to claim 1 tocarry out on a production process any of the operations of: monitoring,controlling, regulating, simulating, optimising, providing support fordecisions, advising.
 46. Use of a method according to claim 1 to carryout on a pulp and/or paper production process any of the operations of:monitoring, controlling, regulating, simulating, optimising, providingsupport for decisions, advising.
 47. Use of a system according to claim25 to carry out on a production process any of the operations of:monitoring, controlling, regulating, simulating, optimising, providingsupport for decisions, advising.
 48. Use of a system according to claim25 to carry out on a pulp and/or paper production process any of theoperations of: monitoring, controlling, regulating, simulating,optimising, providing support for decisions, advising.